Papers
2
Total Citations
14
H-Index
2
About
Xuzhong Hu is a researcher advancing the frontier of autonomous driving and robotic perception, with a focus on multi-sensor fusion and environmental robustness. His work centers on two critical challenges: achieving precise, automated calibration between LiDAR and camera systems, and enabling reliable perception in degraded visual conditions like fog. Hu’s most-cited paper, “A Robust LiDAR-Camera Self-Calibration Via Rotation-Based Alignment and Multi-Level Cost Volume” (2023, 12 citations), addresses the labor-intensive nature of traditional sensor calibration. By introducing a rotation-based alignment method coupled with a multi-level cost volume, he provides a fully automated solution that is both accurate and robust, a foundational contribution for multi-sensor collaborative perception in self-driving and navigation. In his more recent work, “Towards Visibility Estimation and Noise-Distribution-Based Defogging for LiDAR in Autonomous Driving” (2024), Hu tackles the noise introduced by fog droplets, which degrades point cloud quality. By linking fog attenuation to visibility, he develops a defogging method that enhances sensor reliability in adverse weather. With these contributions, Hu is helping to build safer, more resilient autonomous systems, demonstrating a clear impact on practical, real-world deployment.
Research Focus
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Top Papers
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